Science mapping of COVID-19 contributions in primary health care by OECD countries: A machine learning approach

Muhammet Damar; Benita Hosseini; Andrew David Pinto; Omer Aydin; Umit Cali

doi:10.1177/20552076251389341

Science mapping of COVID-19 contributions in primary health care by OECD countries: A machine learning approach

Digit Health. 2025 Oct 27:11:20552076251389341. doi: 10.1177/20552076251389341. eCollection 2025 Jan-Dec.

Authors

Muhammet Damar^{1

2}, Benita Hosseini^{2

3

4}, Andrew David Pinto^{2

3

4

5}, Omer Aydin^{6

7}, Umit Cali^{8

9}

Affiliations

¹ Fethiye Faculty of Business Administration, Management Information Systems, Mugla Sitki Kocman University, Mugla, Türkiye.
² Upstream Lab, MAP, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada.
³ Department of Family and Community Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁴ Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
⁵ Department of Family and Community Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
⁶ Computer Science, Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Manisa Celal Bayar University, Manisa, Turkiye.
⁷ Department of Management Sciences and Engineering, University of Waterloo, Waterloo, Canada.
⁸ Department of Electric Power Engineering, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
⁹ School of Physics, Engineering and Technology, York University, York, UK.

Abstract

Purpose: Our study comprehensively assesses how Canada and Organisation for Economic Co-operation and Development (OECD) countries have supported researchers, research institutes and their scientific productivity in primary health care (PHC), one of the areas most affected by COVID-19.

Method: We analyzed research contributions among OECD countries and assessed their scientific productivity during COVID-19 using bibliometric methods and machine learning techniques. Our analysis includes co-authorship networks, funding patterns, co-citation analysis, thematic mapping, factor analysis, and topic modeling through latent Dirichlet allocation.

Results: This study analyzes 1061 articles and review papers involving 5765 researchers from OECD countries. PHC systems played a crucial role in the global response to SARS-CoV2 but faced significant challenges. Canada ranks third in PHC research output and forth in COVID-19 research among OECD nations. The findings reveal Canada's strong collaborative ties with countries such as the USA, UK, and Australia. However, disparities in PHC scientific productivity across OECD countries remain, with some nations showing minimal progress.

Conclusions: Our study highlights the importance of academic collaboration in addressing pandemic-related crises. The study recommends enhancing international collaboration, led by countries such as Canada, the USA, and the UK, to strengthen PHC systems during global health crises. It is deemed necessary to include experts and academics from the field of PHC in such structures. It also emphasizes the need for academic journals to improve transparency in funding sources through automated extraction of bibliometric data from platforms such as Web of Science and Scopus, which is crucial for shaping future health and education policies.

Keywords: COVID-19; Canada; OECD countries; machine learning; primary health care; research funding; scientific productivity.